A logic board electrical performance multi-parameter data fusion test and precision compensation regulation method

Abstract

The present application belongs to the technical field of electronic testing and measurement, and specifically relates to a logic board electrical performance multi-parameter data fusion testing and precision compensation regulation method, which mainly comprises the following steps: step one: a multi-channel synchronous acquisition link is built, and core electrical performance parameters of the logic board are collected in parallel, time sequence and working conditions are ensured to be homologous, a parameter coupling hard constraint model is established in combination with circuit topology and device characteristics, a correlation threshold and a mapping relationship are calibrated, noise redundant data is removed, and an effective test interval is locked; step two: a logic of hierarchical feature extraction, normalization alignment and coupling matching verification is adopted, performance is determined through parameter linkage adaptability, hidden defects are accurately identified, and real electrical performance of the logic board is comprehensively reflected; and step five: a self-consistent closed loop system without external standard source is built, compensated data and a reference library are cross-checked, each regulation parameter is optimized reversely, and a full-process closed loop iteration is formed.

Description

Technical Field

[0001] This invention belongs to the field of electronic testing and measurement technology, specifically a method for multi-parameter data fusion testing and accuracy compensation control of logic board electrical performance. Background Technology

[0002] As the core hardware carrier of electronic devices such as industrial control, communication base stations, intelligent displays, automotive electronics, and server systems, the logic board undertakes key functions such as signal transmission and reception, logic operation, level conversion, and timing control.

[0003] As electronic devices iterate and upgrade towards higher precision, higher integration, and higher reliability, increasingly stringent requirements are placed on the comprehensiveness, accuracy, and consistency of logic board electrical performance testing. Multiple electrical performance parameters, such as voltage amplitude, timing characteristics, impedance matching, power supply ripple, leakage current, and power consumption characteristics, directly determine the operational stability, working accuracy, and service life of the equipment. They are core testing items in product factory quality inspection, process quality control, and fault diagnosis.

[0004] Currently, conventional logic board electrical performance testing methods in the industry mostly adopt a mode of independent acquisition of single parameters and separate judgment. Various electrical performance parameters are tested and calibrated separately, failing to achieve the correlation, fusion, and comprehensive analysis of multi-dimensional parameters. On the one hand, there are coupling correlations between various electrical performance parameters. Single-parameter testing severs the inherent connection between parameters and cannot fully reflect the overall electrical performance status of the logic board. This easily leads to missed detections and misjudgments, making it difficult to accurately identify hidden performance defects. On the other hand, the test data is isolated and fragmented, making it impossible to optimize the test judgment logic through multi-parameter collaborative analysis. The reliability and comprehensiveness of the test results are insufficient. Therefore, this invention provides a method for multi-parameter data fusion testing and accuracy compensation control of logic board electrical performance. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0006] The technical solution adopted by this invention to solve its technical problem is: a method for multi-parameter data fusion testing and precision compensation control of logic board electrical performance, which mainly includes the following steps: Step 1: Build a multi-channel synchronous acquisition link to acquire the core electrical performance parameters of the logic board in parallel, ensuring that the timing and operating conditions are consistent. Combine the circuit topology and device characteristics to establish a parameter coupling hard constraint model, calibrate the correlation threshold and mapping relationship, eliminate noise and redundant data, and lock in the effective test range. Step 2: Employing a logic of hierarchical feature extraction, normalization alignment, and coupled matching verification, performance is determined through parameter linkage adaptability, accurately identifying hidden defects and comprehensively reflecting the true electrical performance of the logic board. Step 3: Decompose the test error into inherent systematic error and random disturbance error, establish identification models for each, peel off the inherent error based on the offline benchmark, monitor the source of disturbance error in real time, accurately distinguish various interference components, and avoid error coupling interference compensation effect; Step 4: Adaptive variable step size recursive compensation is used to make a fixed-value one-time correction to the inherent system error, and for random disturbance error, the compensation step size is dynamically adjusted according to the magnitude of error fluctuation to eliminate test deviation; Step 5: Build a self-consistent closed-loop system without external standard sources, cross-check the compensated data with the benchmark library, optimize various control parameters in reverse, and form a closed-loop iteration of the entire process.

[0007] Preferably, the multi-channel synchronous acquisition includes: establishing a multi-channel synchronous acquisition link to acquire logic board voltage amplitude, timing phase, impedance matching value, power supply ripple, leakage current, static power consumption and other core electrical performance parameters in parallel, ensuring that the parameter acquisition timing is consistent and the operating conditions are the same, and using a preset filtering algorithm to remove random noise, interference clutter and redundant data introduced during the acquisition process.

[0008] Preferably, the coupling hard constraint includes: establishing a coupling mapping constraint relationship between various electrical performance parameters by combining the logic board hardware circuit topology, device electrical characteristics and design parameters, accurately calibrating the normal correlation threshold between parameters, clarifying the reasonable range of parameter linkage, avoiding misjudgment problems caused by single parameter abnormality through the coupling mapping constraint relationship, and providing a judgment basis for subsequent coupling matching verification.

[0009] Preferably, the hierarchical feature extraction includes: abandoning the conventional multi-parameter weighted summation fusion method, adopting the logic of hierarchical extraction, the first layer extracts the basic feature values ​​of individual electrical performance parameters, and clarifies the independent performance indicators of each parameter, the second layer extracts the correlation features that reflect the coupling relationship between each parameter, and captures the linkage law of parameters. After the extraction is completed, the two layers of features are normalized and aligned to eliminate the dimensional differences of different parameters.

[0010] Preferably, the coupling matching verification specifically involves matching and comparing the basic features extracted and normalized in layers with the associated features through a preset coupling hard constraint rule to determine whether the linkage adaptability of the electrical performance parameters meets the constraint requirements.

[0011] Preferably, the inherent system error is a fixed error that is not affected by changes in test conditions or environmental conditions.

[0012] Preferably, the random disturbance error includes: variable error caused by external factors such as ambient temperature and humidity fluctuations, tooling contact impedance drift, line transmission loss, and electromagnetic interference. By real-time monitoring of test environment parameters and tooling operating status, the source of random disturbance error can be traced and located, and the error components caused by different interference sources can be distinguished, providing support for subsequent accurate compensation.

[0013] Preferably, the variable step size recursive compensation includes: using adaptive control logic to achieve one-time accurate correction of the inherent system error after stripping using a fixed value offset compensation method; for random disturbance errors after source tracing and positioning, dynamically adjusting the compensation step size according to the magnitude of the error component and the rate of change of disturbance in real time; using a large step size for rapid correction when the error fluctuation is large, and using a small step size for fine adjustment when the error tends to stabilize.

[0014] Preferably, the cross-verification specifically involves cross-comparing the test data after variable step-size recursive compensation with a pre-established offline qualified logic board benchmark library, calculating the test deviation value in real time, providing feedback on whether the compensation effect meets the preset accuracy requirements, and using the deviation data as the basis for reverse optimization of the coupled hard constraint model, error identification threshold, and compensation step-size parameters.

[0015] The beneficial effects of this invention are as follows: 1. The present invention provides a method for multi-parameter data fusion testing and precision compensation adjustment of logic board electrical performance, which effectively solves the drawbacks of single-parameter independent testing in existing technologies. By multi-channel synchronous acquisition, parameter coupling hard constraint modeling, and hierarchical feature fusion, it breaks down the barriers between parameters, accurately captures the correlation between parameters, can accurately identify hidden performance defects of logic boards, eliminates missed detections and misjudgments, significantly improves the comprehensiveness and accuracy of electrical performance testing, and fully restores the true electrical performance state of the logic board.

[0016] 2. The method for multi-parameter data fusion testing and accuracy compensation control of logic board electrical performance described in this invention breaks through the limitations of traditional static error compensation. It adopts dual-domain error separation and variable step-size recursive compensation technology to accurately separate inherent system errors and random disturbance errors. The compensation strategy can be dynamically adjusted according to changes in operating conditions and environment, effectively eliminating test deviations caused by various interference factors, significantly improving test accuracy, adapting to complex test conditions, and meeting the stringent test requirements of high-end logic boards.

[0017] 3. The logic board electrical performance multi-parameter data fusion test and accuracy compensation control method described in this invention constructs a self-consistent closed-loop control system without external standard sources, realizing a fully automated closed loop of testing, compensation, verification, and iteration. It eliminates the need for manual calibration, reducing labor costs and continuously optimizing control parameters to ensure consistency and stability in long-term testing. It is suitable for continuous testing scenarios with large batches and multiple operating conditions, improving testing efficiency and practicality. Attached Figure Description

[0018] The invention will now be further described with reference to the accompanying drawings.

[0019] Figure 1 This is a schematic diagram of the control steps in this invention; Figure 2 This is a schematic diagram of the self-consistent closed-loop control process in this invention. Detailed Implementation

[0020] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0021] like Figure 1 As shown in the embodiment of the present invention, a method for multi-parameter data fusion testing and accuracy compensation control of logic board electrical performance mainly includes the following steps: Step 1: Establish a multi-channel synchronous acquisition link to collect core electrical performance parameters of the logic board in parallel, ensuring that timing and operating conditions are consistent. Combining circuit topology and device characteristics, establish a parameter coupling hard constraint model, calibrate correlation thresholds and mapping relationships, eliminate noise and redundant data, and lock in the effective test range. This improves the effectiveness of the acquired data from the source, laying the foundation for subsequent multi-parameter fusion testing. The core function of the parameter coupling hard constraint model is to quantify the inherent correlation between various electrical performance parameters, reducing misjudgments caused by single parameter anomalies. Its coupling mapping relationship can be expressed by the following formula: In the formula: For the first Constraint output values ​​of electrical performance parameters; For the first Collected values ​​of electrical performance parameters; For the first The coefficients of the term parameters themselves; For the first Item and the The coupling coefficient of the parameter is used to characterize the correlation strength between parameters; To constrain the small error term of the model, the value range is ±0.01, which is used to correct model bias. Through this formula, the correlation threshold between parameters can be accurately calibrated. When the collected data is substituted into the formula, if the output value exceeds the preset reasonable range, it is judged as redundant noise data and is removed. Step 2: Abandoning the conventional multi-parameter weighted summation fusion method, we adopt the logic of hierarchical feature extraction, normalization alignment, and coupling matching verification. First, we extract the basic features of individual electrical performance parameters and the coupling correlation features between parameters. Then, we eliminate the dimensional differences of different parameters through normalization processing. We determine the performance through parameter linkage adaptability, accurately identify hidden defects, and comprehensively reflect the true electrical performance of the logic board, solving the problems of missed detection and misjudgment in traditional single-parameter testing. Step 3: Addressing the coupling errors caused by factors such as ambient temperature and humidity, and tooling contact impedance during testing, the total test error is divided into two categories: inherent systematic errors and random disturbance errors. Corresponding error identification models are established for each category to achieve accurate separation of the two types of errors. Specifically, based on offline calibrated qualified logic board baseline data, inherent systematic errors unaffected by operating conditions and environment are removed. Then, by real-time monitoring of test conditions and environmental parameters, the source of random disturbance errors is traced and located, distinguishing error components from different interference sources. This reduces the effectiveness of compensation for the mutual coupling interference of various errors, providing support for subsequent accurate compensation. The error decomposition formula is as follows: In the formula: This represents the total test error; This is an inherent systematic error; As random disturbance error, this formula can accurately decompose the total error into two independent types of error, which can be identified and processed separately to avoid compensation failure caused by error superposition. Step 4: An adaptive variable-step recursive compensation method is adopted to accurately correct both the inherent systematic error after separation and the random disturbance error after tracing its source. For the inherent systematic error, a fixed-value offset compensation method is used to achieve one-time accurate correction. For the random disturbance error, the compensation step size is dynamically adjusted according to the magnitude of the error components and the rate of change of the disturbance in real time. Large step sizes are used for rapid correction when the error fluctuation is large, and small step sizes are used for fine-tuning when the error tends to stabilize. This effectively eliminates test deviations caused by various errors and improves the accuracy of the test data. The variable-step recursive compensation formula is as follows: In the formula: This is the compensation amount for the (n+1)th time; This is the amount of compensation for the nth time; The nth compensation step size is adaptively adjusted, with a value range of 0.001 to 0.1. A larger value is used when the error fluctuation is large, and a smaller value is used when the fluctuation is small. This represents the deviation value for the nth test. Step 5: Build a self-consistent closed-loop system without external standard sources. It does not rely on external standard tooling for calibration. The test data after variable step size recursive compensation is cross-checked with a pre-established offline qualified logic board benchmark library. The test deviation value is calculated in real time, and feedback is given on whether the compensation effect meets the preset accuracy requirements. At the same time, the deviation data is used as the basis for reverse optimization of the coupled hard constraint model, error identification threshold and compensation step size parameters. This forms a closed-loop control of the entire process of acquisition, fusion, identification, compensation, verification and iteration, continuously optimizing the test and compensation accuracy and adapting to continuous testing scenarios with large batches and multiple working conditions.

[0022] like Figure 2 As shown, the multi-channel synchronous acquisition specifically involves establishing a multi-channel synchronous acquisition link to acquire core electrical performance parameters of the logic board in parallel, including voltage amplitude, timing phase, impedance matching value, power ripple, leakage current, and static power consumption. The acquisition frequency is synchronously set to 100kHz~1MHz, which can be adaptively adjusted according to the logic board model to strictly ensure consistent parameter acquisition timing and operating conditions. A preset filtering algorithm is used to remove random noise, interference, and redundant data introduced during the acquisition process, ensuring the effectiveness and accuracy of the acquired data and laying the foundation for subsequent multi-parameter fusion testing. To further improve the effectiveness and accuracy of the acquired data, a preset filtering algorithm is added to the acquisition link, specifically using a Kalman filter algorithm to remove random noise, electromagnetic interference, and redundant data introduced during the acquisition process. The state update formula for the Kalman filter is as follows: In the formula: This represents the filtered valid data from the kth acquisition. This represents the predicted data collected in the kth iteration; Kalman gain This represents the raw data collected in the kth iteration; The observation matrix; For the prediction error covariance; To observe the noise covariance, this filtering algorithm can effectively remove redundant noise data, ensuring the accuracy of the collected data and providing reliable data support for subsequent coupled hard constraint modeling and hierarchical feature fusion.

[0023] like Figure 2As shown, the coupling hard constraint specifically combines the logic board hardware circuit topology, device electrical characteristics, and design parameters. The hardware circuit topology refers to the connection method of the driver circuit and logic operation circuit; the device electrical characteristics refer to the rated voltage and impedance parameters of the chip; and the design parameters refer to the preset timing thresholds and power consumption ranges. A coupling mapping constraint relationship is established between various electrical performance parameters. Through statistical analysis of a large amount of offline test data, the normal correlation threshold between parameters is accurately calibrated, and the reasonable range of parameter linkage is clarified. This constraint relationship avoids misjudgment problems caused by single parameter anomalies and provides a basis for subsequent coupling matching verification. Taking the logic board's voltage amplitude and static power consumption as an example, there is a clear coupling correlation between the two. Changes in voltage amplitude directly affect the magnitude of static power consumption. The coupling mapping constraint relationship can be further refined by the following formula: In the formula: This refers to static power consumption. Voltage amplitude; , , The fitting coefficients are obtained by fitting multiple sets of voltage amplitude and static power consumption data calibrated offline using the least squares method. Through this constraint formula, the reasonable range of static power consumption corresponding to different voltage amplitudes can be clearly defined. If the collected static power consumption exceeds this range, even if the voltage amplitude alone meets the threshold requirement, it is judged as abnormal, thereby avoiding the misjudgment problem caused by a single parameter abnormality, and providing a clear judgment basis for subsequent coupling matching verification.

[0024] like Figure 2 As shown, the hierarchical feature extraction specifically abandons the conventional multi-parameter weighted summation fusion method and adopts a hierarchical extraction logic. The first layer is basic feature extraction, which extracts the core basic feature values ​​for each electrical performance parameter, such as the peak and effective values ​​of voltage amplitude, the offset of timing phase, the fluctuation range of impedance matching value, and the peak and peak values ​​of power supply ripple, clarifying the independent performance indicators of each parameter and providing a foundation for subsequent parameter linkage analysis. The second layer is correlation feature extraction, which focuses on extracting correlation features that reflect the coupling relationship between various parameters, such as the slope of the change in voltage amplitude and static power consumption, and the linkage deviation between timing phase and impedance matching value, capturing the linkage law between parameters and avoiding the one-sidedness caused by single parameter feature extraction. Since the units of different electrical performance parameters are different, such as voltage amplitude in V, power consumption in W, and impedance in Ω, after extraction, the two layers of features need to be normalized and aligned to eliminate the influence of unit differences. The normalization formula is as follows: In the formula: These are the normalized eigenvalues; These are the original eigenvalues; This is the maximum value of the feature; The minimum value of this feature is normalized so that all feature values ​​are within the range of [0,1], which prepares for subsequent coupling matching verification and ensures the accuracy of the verification results.

[0025] like Figure 2 As shown, the coupling matching verification specifically involves matching and comparing the basic features extracted and normalized layer by layer with the associated features through preset coupling hard constraint rules. This determines whether the linkage adaptability of the electrical performance parameters meets the constraint requirements. If the parameter linkage deviation exceeds the constraint range, it accurately identifies the hidden performance defects in the logic board, reducing the missed detection problem caused by relying solely on the threshold of a single parameter. The matching degree calculation formula is as follows: In the formula: The coupling matching degree (within the range of [0,1]); The total number of features; For the first Normalized collected values ​​of the feature; For the first The normalization constraint standard value of the feature; The preset matching degree threshold is 0.85, which can be adaptively adjusted according to the logic board model. When the calculated matching degree m≥0.85, it is determined that the linkage adaptability of each electrical performance parameter meets the constraint requirements and the electrical performance of the logic board is normal. When m<0.85, it is determined that the linkage deviation of the parameter exceeds the constraint range, further locating the source of the deviation and accurately identifying the hidden performance defects of the logic board, such as device aging and poor circuit contact. This effectively reduces the problem of missed detection caused by relying solely on the threshold of a single parameter and comprehensively reflects the true electrical performance status of the logic board.

[0026] like Figure 2 As shown, the inherent system error is a fixed error that is not affected by changes in test conditions or environmental conditions. It mainly originates from the accuracy deviation of the test equipment itself and the inherent loss of the acquisition link. By comparing the baseline data of the qualified logic board calibrated offline with the measured data, the inherent system error can be removed in one go, ensuring the accuracy of error separation. The specific implementation process is as follows: Multiple, but no fewer than five, qualified logic boards were selected as reference samples. Under standard conditions (temperature 25℃±2℃, humidity 50%±5%, no electromagnetic interference), offline calibration was performed using the aforementioned multi-channel synchronous acquisition link. Reference data for various electrical performance parameters were collected, and the average value of the reference data was calculated as the standard value. The raw data collected during the actual testing process Compared with standard value The difference is the inherent systematic error. The calculation formula is as follows: This formula can accurately isolate the inherent systematic error of each electrical performance parameter, ensuring the accuracy of error separation and providing a clear error basis for subsequent variable step-size recursive compensation, thereby reducing the compensation deviation caused by the superposition of inherent systematic errors and random disturbance errors.

[0027] like Figure 2 As shown, the random disturbance error is specifically a variable error caused by external factors such as fluctuations in ambient temperature and humidity, drift in tooling contact impedance, line transmission loss, and electromagnetic interference. By monitoring the test environment parameters and the tooling operating status in real time, the source of the random disturbance error is traced and located, and the error components brought by different interference sources are distinguished, providing support for subsequent accurate compensation. The specific implementation process is as follows: An environmental monitoring module (monitoring temperature and humidity), a tooling status monitoring module (monitoring contact impedance), and a line loss monitoring module (monitoring transmission loss) are added to the testing system to collect parameter values ​​of various interference factors in real time. A correlation model between random disturbance errors and interference factors is established to trace and locate the error component caused by each interference factor. The correlation model is as follows: In the formula: This is random disturbance error; Types of interfering factors; Let be the error coefficient of the kth interference factor; The real-time monitoring value of the k-th interference factor at time t is given. Through this correlation model, the error components caused by different interference sources can be accurately distinguished, such as the error components caused by temperature and humidity fluctuations and the error components caused by tooling contact impedance drift. This reduces the mutual coupling interference compensation effect of various errors and provides targeted error basis for subsequent accurate compensation.

[0028] like Figure 2 As shown, the variable step-size recursive compensation specifically employs adaptive control logic. For the inherent system error after stripping, a fixed-value offset compensation method is used to achieve one-time precise correction. For the random disturbance error after source tracing and localization, the compensation step size is dynamically adjusted based on the magnitude of the error components and the rate of change of the disturbance under real-time monitoring. When the error fluctuation is large, a large step size is used for rapid correction; when the error tends to stabilize, a small step size is used for fine-tuning, achieving precise correction of both types of errors. Specific implementation details are as follows: Addressing the inherent systematic errors after stripping A fixed-value offset compensation method is used to achieve one-time accurate correction. The compensation formula is as follows: In the formula: The test data is after compensation for inherent system errors; This is the original collected data; This refers to the inherent systematic error after stripping. For random disturbance errors after source tracing and localization An adaptive variable step-size recursive compensation method is adopted, which dynamically adjusts the compensation step size according to the magnitude of the error components and the rate of change of disturbances monitored in real time. The formula for adjusting the compensation step size is as follows: In the formula: The initial step size is set to 0.1 by default. This is the step size adjustment factor, with a value ranging from 0.5 to 1.0; For the first The deviation value of the second test, when the error fluctuation is large ( (larger) Approaching Large step size is used for rapid correction; when the error tends to stabilize ( (smaller) The error is reduced by using small-step fine-tuning to ultimately achieve accurate compensation for random disturbance errors. The compensated test data... for: The above compensation methods can effectively eliminate test deviations caused by various errors, significantly improve the accuracy of test data, and adapt to complex test conditions with multiple coupled parameters.

[0029] like Figure 2 As shown, the cross-verification specifically involves comparing the test data after variable step-size recursive compensation with a pre-established offline qualified logic board benchmark library, calculating the test deviation value in real time, and providing feedback on whether the compensation effect meets the preset accuracy requirements. The deviation data serves as the basis for reverse optimization of the coupled hard constraint model, error identification threshold, and compensation step-size parameters, providing data support for the closed-loop control of the entire process. Specific implementation details are as follows: First, a baseline library of qualified offline logic boards is pre-established. A large number of qualified logic boards, no fewer than 50, are selected and offline tested in a standard environment. Compensated data for various electrical performance parameters are collected, and after statistical analysis, a baseline library is established. This library contains the standard ranges and core data of coupling constraint thresholds for each parameter. During actual testing, the test data after variable-step recursive compensation is used. It cross-references the data with standard data in the benchmark library and calculates the test deviation value in real time. The formula for calculating the deviation value is as follows: In the formula: For the first The average deviation of the tests; Types of electrical performance parameters; For the first Test data after compensation for the parameters; For the first in the benchmark library Standard values ​​for the parameter; Preset accuracy threshold The value ranges from 0.02 to 0.05. When the compensation effect reaches the preset accuracy requirement, it is determined that the compensation effect has reached the preset accuracy requirement; when If the feedback compensation effect fails to meet the standard, the deviation data will be... As a basis for inverse optimization, the coupling coefficient of the coupled hard-constraint model is used. Error identification threshold, initial value of compensation step size Adaptive adjustment is performed using the following formula: In the formula: , To optimize the coefficients, the values ​​are all between 0.1 and 0.3; , These are the optimized parameter values; , These are the parameter values ​​before optimization. Through the above cross-verification and reverse optimization, a closed-loop control system is formed that integrates data collection, fusion, identification, compensation, verification, and iteration. This eliminates the need for manual calibration, continuously optimizes testing and compensation accuracy, provides reliable data support for the closed-loop control system, and is suitable for continuous testing scenarios with large batches and multiple operating conditions.

[0030] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for multi-parameter data fusion testing and precision compensation control of logic board electrical performance, characterized in that, This method mainly includes the following steps: Step 1: Build a multi-channel synchronous acquisition link to acquire the core electrical performance parameters of the logic board in parallel, ensuring that the timing and operating conditions are consistent. Combine the circuit topology and device characteristics to establish a parameter coupling hard constraint model, calibrate the correlation threshold and mapping relationship, eliminate noise and redundant data, and lock in the effective test range. Step 2: Employing a hierarchical feature extraction, normalization alignment, and coupled matching verification logic, performance is determined through parameter linkage adaptability, accurately identifying hidden defects and comprehensively reflecting the true electrical performance of the logic board. Step 3: Decompose the test error into inherent systematic error and random disturbance error, establish identification models for each, peel off the inherent error based on the offline benchmark, monitor the source of disturbance error in real time, accurately distinguish various interference components, and avoid error coupling interference compensation effect; Step 4: Adaptive variable step size recursive compensation is used to make a fixed-value one-time correction to the inherent system error, and for random disturbance error, the compensation step size is dynamically adjusted according to the magnitude of error fluctuation to eliminate test deviation; Step 5: Build a self-consistent closed-loop system without external standard sources, cross-check the compensated data with the benchmark library, optimize various control parameters in reverse, and form a closed-loop iteration of the entire process.

2. The method for multi-parameter data fusion testing and precision compensation control of logic board electrical performance according to claim 1, characterized in that: The multi-channel synchronous acquisition includes: establishing a multi-channel synchronous acquisition link to acquire logic board voltage amplitude, timing phase, impedance matching value, power supply ripple, leakage current, static power consumption and other core electrical performance parameters in parallel, ensuring that the parameter acquisition timing is consistent and the operating conditions are the same, and using a preset filtering algorithm to remove random noise, interference clutter and redundant data introduced during the acquisition process.

3. The method for multi-parameter data fusion testing and precision compensation control of logic board electrical performance according to claim 1, characterized in that: The coupling hard constraints include: establishing coupling mapping constraints between various electrical performance parameters by combining the logic board hardware circuit topology, device electrical characteristics and design parameters, accurately calibrating the normal correlation threshold between parameters, clarifying the reasonable range of parameter linkage, avoiding misjudgment problems caused by single parameter anomalies through coupling mapping constraints, and providing a judgment basis for subsequent coupling matching verification.

4. The method for multi-parameter data fusion testing and precision compensation control of logic board electrical performance according to claim 1, characterized in that: The hierarchical feature extraction includes: abandoning the conventional multi-parameter weighted summation fusion method, adopting the logic of hierarchical extraction. The first layer extracts the basic feature values ​​of individual electrical performance parameters and clarifies the independent performance indicators of each parameter. The second layer extracts the correlation features that reflect the coupling relationship between various parameters and captures the linkage law of parameters. After the extraction is completed, the two layers of features are normalized and aligned to eliminate the dimensional differences of different parameters.

5. The method for multi-parameter data fusion testing and precision compensation control of logic board electrical performance according to claim 1, characterized in that: The coupling matching verification specifically involves matching and comparing the basic features extracted and normalized in layers with the associated features through preset coupling hard constraint rules to determine whether the linkage adaptability of electrical performance parameters meets the constraint requirements.

6. The method for multi-parameter data fusion testing and precision compensation control of logic board electrical performance according to claim 1, characterized in that: The inherent system error is specifically a fixed error that is not affected by changes in test conditions or environmental conditions.

7. The method for multi-parameter data fusion testing and precision compensation control of logic board electrical performance according to claim 1, characterized in that: The random disturbance error includes variable errors caused by external factors such as ambient temperature and humidity fluctuations, tooling contact impedance drift, line transmission loss, and electromagnetic interference. By monitoring the test environment parameters and tooling operating status in real time, the source of the random disturbance error can be traced and located, and the error components caused by different interference sources can be distinguished, providing support for subsequent accurate compensation.

8. The method for multi-parameter data fusion testing and precision compensation control of logic board electrical performance according to claim 1, characterized in that: The variable step size recursive compensation includes: using adaptive control logic, the inherent system error after stripping is corrected in one go by a fixed value offset compensation method; for random disturbance errors after source tracing and positioning, the compensation step size is dynamically adjusted according to the magnitude of the error component and the rate of change of the disturbance in real time; when the error fluctuation is large, a large step size is used for rapid correction; when the error tends to be stable, a small step size is used for fine adjustment.

9. The method for multi-parameter data fusion testing and precision compensation control of logic board electrical performance according to claim 1, characterized in that: The cross-verification specifically involves comparing the test data after variable step-size recursive compensation with a pre-established offline qualified logic board benchmark library, calculating the test deviation value in real time, and providing feedback on whether the compensation effect meets the preset accuracy requirements. The deviation data serves as the basis for reverse optimization of the coupled hard constraint model, error identification threshold, and compensation step-size parameters.

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